Overview

Dataset statistics

Number of variables21
Number of observations203973
Missing cells1410888
Missing cells (%)32.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.7 MiB
Average record size in memory168.0 B

Variable types

NUM14
CAT6
DATE1

Warnings

wind_spd has a high cardinality: 2186 distinct values High cardinality
pct_possible has a high cardinality: 102 distinct values High cardinality
wind_chill is highly correlated with tempHigh correlation
temp is highly correlated with wind_chillHigh correlation
sta_press is highly correlated with sea_lvl_press and 1 other fieldsHigh correlation
sea_lvl_press is highly correlated with sta_press and 1 other fieldsHigh correlation
altimeter_setting is highly correlated with sea_lvl_press and 1 other fieldsHigh correlation
file_name is highly correlated with site and 1 other fieldsHigh correlation
site is highly correlated with file_nameHigh correlation
year is highly correlated with file_nameHigh correlation
dew_pt has 31293 (15.3%) missing values Missing
rH has 31361 (15.4%) missing values Missing
heat_idx has 200433 (98.3%) missing values Missing
wind_chill has 132253 (64.8%) missing values Missing
wind_dir has 10967 (5.4%) missing values Missing
hr_precip has 152903 (75.0%) missing values Missing
snow_depth has 75498 (37.0%) missing values Missing
snowfall_3hr has 76765 (37.6%) missing values Missing
snowfall_6hr has 77147 (37.8%) missing values Missing
snowfall_24hr has 77582 (38.0%) missing values Missing
sea_lvl_press has 129512 (63.5%) missing values Missing
sta_press has 104548 (51.3%) missing values Missing
altimeter_setting has 104548 (51.3%) missing values Missing
solar_radiation has 103039 (50.5%) missing values Missing
pct_possible has 103039 (50.5%) missing values Missing
hr_precip is highly skewed (γ1 = 77.15931459) Skewed
snowfall_3hr is highly skewed (γ1 = 42.5850237) Skewed
snowfall_6hr is highly skewed (γ1 = 38.03277476) Skewed
snowfall_24hr is highly skewed (γ1 = 23.63135992) Skewed
hr_precip has 45920 (22.5%) zeros Zeros
snow_depth has 10522 (5.2%) zeros Zeros
snowfall_3hr has 83847 (41.1%) zeros Zeros
snowfall_6hr has 81761 (40.1%) zeros Zeros
snowfall_24hr has 78076 (38.3%) zeros Zeros
solar_radiation has 51952 (25.5%) zeros Zeros

Reproduction

Analysis started2023-01-11 15:47:06.542181
Analysis finished2023-01-11 15:47:42.416190
Duration35.87 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

dt
Date

Distinct79353
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2007-01-01 00:00:00
Maximum2022-12-31 16:00:00
2023-01-11T10:47:42.500779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:42.630219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Real number (ℝ)

HIGH CORRELATION

Distinct152
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.59389723
Minimum-49
Maximum106
Zeros592
Zeros (%)0.3%
Memory size1.6 MiB
2023-01-11T10:47:42.764665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-49
5-th percentile7
Q123
median34
Q350
95-th percentile72
Maximum106
Range155
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.00827821
Coefficient of variation (CV)0.5467654369
Kurtosis0.04882038782
Mean36.59389723
Median Absolute Deviation (MAD)13
Skewness0.1567569316
Sum7464167
Variance400.3311968
MonotocityNot monotonic
2023-01-11T10:47:42.882797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3255952.7%
 
3051512.5%
 
3150862.5%
 
2850472.5%
 
2749572.4%
 
2948922.4%
 
3348562.4%
 
2647032.3%
 
2544392.2%
 
2442682.1%
 
Other values (142)15497976.0%
 
ValueCountFrequency (%) 
-491< 0.1%
 
-482< 0.1%
 
-471< 0.1%
 
-451< 0.1%
 
-441< 0.1%
 
ValueCountFrequency (%) 
1061< 0.1%
 
1042< 0.1%
 
1021< 0.1%
 
1012< 0.1%
 
1005< 0.1%
 

dew_pt
Real number (ℝ)

MISSING

Distinct153
Distinct (%)0.1%
Missing31293
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean26.25127982
Minimum-76
Maximum101
Zeros582
Zeros (%)0.3%
Memory size1.6 MiB
2023-01-11T10:47:43.003587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-76
5-th percentile1
Q118
median27
Q337
95-th percentile49
Maximum101
Range177
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.47150367
Coefficient of variation (CV)0.5893618815
Kurtosis3.316775538
Mean26.25127982
Median Absolute Deviation (MAD)9
Skewness-0.9906090565
Sum4533071
Variance239.3674258
MonotocityNot monotonic
2023-01-11T10:47:43.120219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2653922.6%
 
2851312.5%
 
2451302.5%
 
3051072.5%
 
2550742.5%
 
3249522.4%
 
2348352.4%
 
2046772.3%
 
2246542.3%
 
2146212.3%
 
Other values (143)12310760.4%
 
(Missing)3129315.3%
 
ValueCountFrequency (%) 
-762< 0.1%
 
-752< 0.1%
 
-744< 0.1%
 
-734< 0.1%
 
-7211< 0.1%
 
ValueCountFrequency (%) 
1011< 0.1%
 
751< 0.1%
 
745< 0.1%
 
736< 0.1%
 
726< 0.1%
 

rH
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)0.1%
Missing31361
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean72.71190879
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2023-01-11T10:47:43.252714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q155
median80
Q392
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation23.10823166
Coefficient of variation (CV)0.317805323
Kurtosis-0.5249556732
Mean72.71190879
Median Absolute Deviation (MAD)15
Skewness-0.7488090353
Sum12550948
Variance533.9903703
MonotocityNot monotonic
2023-01-11T10:47:43.373340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10071183.5%
 
9461133.0%
 
9259212.9%
 
9658302.9%
 
9556812.8%
 
9354402.7%
 
9050072.5%
 
9148382.4%
 
9747222.3%
 
9841182.0%
 
Other values (90)11782457.8%
 
(Missing)3136115.4%
 
ValueCountFrequency (%) 
11630.1%
 
22250.1%
 
31830.1%
 
41070.1%
 
551< 0.1%
 
ValueCountFrequency (%) 
10071183.5%
 
9933891.7%
 
9841182.0%
 
9747222.3%
 
9658302.9%
 

heat_idx
Real number (ℝ≥0)

MISSING

Distinct29
Distinct (%)0.8%
Missing200433
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean82.53700565
Minimum78
Maximum201
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2023-01-11T10:47:43.480454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile79
Q180
median82
Q384
95-th percentile89
Maximum201
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.965097524
Coefficient of variation (CV)0.04804023957
Kurtosis227.5681745
Mean82.53700565
Median Absolute Deviation (MAD)2
Skewness8.743039208
Sum292181
Variance15.72199838
MonotocityNot monotonic
2023-01-11T10:47:43.585684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
807320.4%
 
815640.3%
 
824320.2%
 
794160.2%
 
833160.2%
 
842560.1%
 
852030.1%
 
861680.1%
 
871130.1%
 
881030.1%
 
Other values (19)2370.1%
 
(Missing)20043398.3%
 
ValueCountFrequency (%) 
7843< 0.1%
 
794160.2%
 
807320.4%
 
815640.3%
 
824320.2%
 
ValueCountFrequency (%) 
2011< 0.1%
 
1101< 0.1%
 
1071< 0.1%
 
1044< 0.1%
 
1022< 0.1%
 

wind_chill
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct112
Distinct (%)0.2%
Missing132253
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean15.35204964
Minimum-70
Maximum42
Zeros798
Zeros (%)0.4%
Memory size1.6 MiB
2023-01-11T10:47:43.704692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-70
5-th percentile-14
Q18
median17
Q326
95-th percentile37
Maximum42
Range112
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.94270288
Coefficient of variation (CV)1.038473901
Kurtosis2.301358773
Mean15.35204964
Median Absolute Deviation (MAD)9
Skewness-1.173803657
Sum1101049
Variance254.1697751
MonotocityNot monotonic
2023-01-11T10:47:43.859627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1625441.2%
 
1924791.2%
 
1823351.1%
 
2122701.1%
 
2222541.1%
 
1422291.1%
 
2421071.0%
 
1120091.0%
 
1020071.0%
 
1319290.9%
 
Other values (102)4955724.3%
 
(Missing)13225364.8%
 
ValueCountFrequency (%) 
-704< 0.1%
 
-694< 0.1%
 
-684< 0.1%
 
-673< 0.1%
 
-662< 0.1%
 
ValueCountFrequency (%) 
421630.1%
 
414790.2%
 
404420.2%
 
3911120.5%
 
3810290.5%
 

wind_dir
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing10967
Missing (%)5.4%
Memory size1.6 MiB
WSW
36888 
SW
34848 
SSW
19673 
NW
17057 
WNW
13352 
Other values (11)
71188 
ValueCountFrequency (%) 
WSW3688818.1%
 
SW3484817.1%
 
SSW196739.6%
 
NW170578.4%
 
WNW133526.5%
 
W126206.2%
 
S86764.3%
 
NE75183.7%
 
NNE73493.6%
 
NNW70463.5%
 
Other values (6)2797913.7%
 
(Missing)109675.4%
 
2023-01-11T10:47:43.974912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-11T10:47:44.071766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.359964309
Min length1

wind_spd
Categorical

HIGH CARDINALITY

Distinct2186
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0G0
 
5369
1G4
 
5353
1G3
 
4677
1G5
 
3633
2G6
 
3500
Other values (2181)
181441 
ValueCountFrequency (%) 
0G053692.6%
 
1G453532.6%
 
1G346772.3%
 
1G536331.8%
 
2G635001.7%
 
2G734341.7%
 
2G532031.6%
 
0G231951.6%
 
2G828501.4%
 
3G826691.3%
 
Other values (2176)16609081.4%
 
2023-01-11T10:47:44.194875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique693 ?
Unique (%)0.3%
2023-01-11T10:47:44.297528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length3.929485765
Min length1

hr_precip
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct50
Distinct (%)0.1%
Missing152903
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean0.005847464265
Minimum0
Maximum19.58
Zeros45920
Zeros (%)22.5%
Memory size1.6 MiB
2023-01-11T10:47:44.397069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum19.58
Range19.58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1995774027
Coefficient of variation (CV)34.13058955
Kurtosis6392.244838
Mean0.005847464265
Median Absolute Deviation (MAD)0
Skewness77.15931459
Sum298.63
Variance0.03983113967
MonotocityNot monotonic
2023-01-11T10:47:44.507757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04592022.5%
 
0.0123421.1%
 
0.029800.5%
 
0.035310.3%
 
0.043770.2%
 
0.052550.1%
 
0.061900.1%
 
0.071050.1%
 
0.0882< 0.1%
 
0.0968< 0.1%
 
Other values (40)2200.1%
 
(Missing)15290375.0%
 
ValueCountFrequency (%) 
04592022.5%
 
0.0123421.1%
 
0.029800.5%
 
0.035310.3%
 
0.043770.2%
 
ValueCountFrequency (%) 
19.581< 0.1%
 
17.751< 0.1%
 
17.531< 0.1%
 
16.481< 0.1%
 
15.641< 0.1%
 

snow_depth
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1178
Distinct (%)0.9%
Missing75498
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean24.66734306
Minimum0
Maximum495.5
Zeros10522
Zeros (%)5.2%
Memory size1.6 MiB
2023-01-11T10:47:44.622811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.4
median12.8
Q338.6
95-th percentile77.9
Maximum495.5
Range495.5
Interquartile range (IQR)34.2

Descriptive statistics

Standard deviation26.87865895
Coefficient of variation (CV)1.089645483
Kurtosis5.692131014
Mean24.66734306
Median Absolute Deviation (MAD)11.5
Skewness1.5359546
Sum3169136.9
Variance722.462307
MonotocityNot monotonic
2023-01-11T10:47:45.043234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0105225.2%
 
0.112480.6%
 
0.310590.5%
 
0.28810.4%
 
0.46780.3%
 
2.56700.3%
 
4.35880.3%
 
45790.3%
 
4.45610.3%
 
4.25570.3%
 
Other values (1168)11113254.5%
 
(Missing)7549837.0%
 
ValueCountFrequency (%) 
0105225.2%
 
0.112480.6%
 
0.28810.4%
 
0.310590.5%
 
0.46780.3%
 
ValueCountFrequency (%) 
495.51< 0.1%
 
479.61< 0.1%
 
4521< 0.1%
 
443.71< 0.1%
 
424.31< 0.1%
 

snowfall_3hr
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct389
Distinct (%)0.3%
Missing76765
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean0.4944610402
Minimum0
Maximum407.4
Zeros83847
Zeros (%)41.1%
Memory size1.6 MiB
2023-01-11T10:47:45.154086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.9
Maximum407.4
Range407.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.743690728
Coefficient of variation (CV)9.593659242
Kurtosis2543.494672
Mean0.4944610402
Median Absolute Deviation (MAD)0
Skewness42.5850237
Sum62899.4
Variance22.50260172
MonotocityNot monotonic
2023-01-11T10:47:45.261710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08384741.1%
 
0.180353.9%
 
0.255292.7%
 
0.340022.0%
 
0.432861.6%
 
0.525231.2%
 
0.620901.0%
 
0.718270.9%
 
0.816260.8%
 
0.912660.6%
 
Other values (379)131776.5%
 
(Missing)7676537.6%
 
ValueCountFrequency (%) 
08384741.1%
 
0.180353.9%
 
0.255292.7%
 
0.340022.0%
 
0.432861.6%
 
ValueCountFrequency (%) 
407.41< 0.1%
 
401.61< 0.1%
 
398.51< 0.1%
 
3831< 0.1%
 
379.21< 0.1%
 

snowfall_6hr
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct433
Distinct (%)0.3%
Missing77147
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean0.6838818539
Minimum0
Maximum495.5
Zeros81761
Zeros (%)40.1%
Memory size1.6 MiB
2023-01-11T10:47:45.373355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile2.6
Maximum495.5
Range495.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation6.255344767
Coefficient of variation (CV)9.146820802
Kurtosis1986.370717
Mean0.6838818539
Median Absolute Deviation (MAD)0
Skewness38.03277476
Sum86734
Variance39.12933815
MonotocityNot monotonic
2023-01-11T10:47:45.483627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08176140.1%
 
0.163913.1%
 
0.247202.3%
 
0.335841.8%
 
0.430501.5%
 
0.524071.2%
 
0.621621.1%
 
0.718610.9%
 
0.817080.8%
 
0.914180.7%
 
Other values (423)177648.7%
 
(Missing)7714737.8%
 
ValueCountFrequency (%) 
08176140.1%
 
0.163913.1%
 
0.247202.3%
 
0.335841.8%
 
0.430501.5%
 
ValueCountFrequency (%) 
495.51< 0.1%
 
479.61< 0.1%
 
424.31< 0.1%
 
410.61< 0.1%
 
399.81< 0.1%
 

snowfall_24hr
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct451
Distinct (%)0.4%
Missing77582
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean1.132665301
Minimum0
Maximum487.1
Zeros78076
Zeros (%)38.3%
Memory size1.6 MiB
2023-01-11T10:47:45.590949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.7
95-th percentile4.5
Maximum487.1
Range487.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation7.181039728
Coefficient of variation (CV)6.339948549
Kurtosis816.2880054
Mean1.132665301
Median Absolute Deviation (MAD)0
Skewness23.63135992
Sum143158.7
Variance51.56733158
MonotocityNot monotonic
2023-01-11T10:47:45.706073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07807638.3%
 
0.138171.9%
 
0.233021.6%
 
0.328571.4%
 
0.425941.3%
 
0.520851.0%
 
0.619030.9%
 
0.717780.9%
 
0.816860.8%
 
0.913690.7%
 
Other values (441)2692413.2%
 
(Missing)7758238.0%
 
ValueCountFrequency (%) 
07807638.3%
 
0.138171.9%
 
0.233021.6%
 
0.328571.4%
 
0.425941.3%
 
ValueCountFrequency (%) 
487.11< 0.1%
 
443.31< 0.1%
 
418.31< 0.1%
 
393.41< 0.1%
 
382.91< 0.1%
 

site
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
SNSLP
75498 
JVEMT
26167 
S11MT
26048 
MRPMT
25744 
SH7MT
25612 
ValueCountFrequency (%) 
SNSLP7549837.0%
 
JVEMT2616712.8%
 
S11MT2604812.8%
 
MRPMT2574412.6%
 
SH7MT2561212.6%
 
SH4MT2490412.2%
 
2023-01-11T10:47:45.813028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-11T10:47:45.876760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:45.960234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

file_name
Categorical

HIGH CORRELATION

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
SNSLP-2016.html
 
8764
JVEMT-2020.html
 
8724
S11MT-2020.html
 
8710
SNSLP-2017.html
 
8558
MRPMT-2020.html
 
8362
Other values (29)
160855 
ValueCountFrequency (%) 
SNSLP-2016.html87644.3%
 
JVEMT-2020.html87244.3%
 
S11MT-2020.html87104.3%
 
SNSLP-2017.html85584.2%
 
MRPMT-2020.html83624.1%
 
SH7MT-2020.html83244.1%
 
SH7MT-2022.html83214.1%
 
MRPMT-2022.html83204.1%
 
JVEMT-2022.html83144.1%
 
S11MT-2022.html82304.0%
 
Other values (24)11934658.5%
 
2023-01-11T10:47:46.071784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-11T10:47:46.177872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

year
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2020
49692 
2022
48814 
2021
42086 
2019
16560 
2016
8764 
Other values (9)
38057 
ValueCountFrequency (%) 
20204969224.4%
 
20224881423.9%
 
20214208620.6%
 
2019165608.1%
 
201687644.3%
 
201785584.2%
 
201871393.5%
 
200862143.0%
 
200755912.7%
 
201435361.7%
 
Other values (4)70193.4%
 
2023-01-11T10:47:46.271831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-11T10:47:46.358477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

sea_lvl_press
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13939
Distinct (%)18.7%
Missing129512
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean969.3133442
Minimum21
Maximum1193.98
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2023-01-11T10:47:46.457668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile775.44
Q1968.93
median1012.43
Q31023.76
95-th percentile1078.14
Maximum1193.98
Range1172.98
Interquartile range (IQR)54.83

Descriptive statistics

Standard deviation103.3706377
Coefficient of variation (CV)0.1066431596
Kurtosis0.05560112754
Mean969.3133442
Median Absolute Deviation (MAD)14.75
Skewness-1.148955857
Sum72176040.92
Variance10685.48873
MonotocityNot monotonic
2023-01-11T10:47:46.573231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1015.4697< 0.1%
 
1015.1280< 0.1%
 
1011.9273< 0.1%
 
1015.870< 0.1%
 
101967< 0.1%
 
101667< 0.1%
 
1017.4865< 0.1%
 
1018.1665< 0.1%
 
1012.2662< 0.1%
 
1017.8961< 0.1%
 
Other values (13929)7375436.2%
 
(Missing)12951263.5%
 
ValueCountFrequency (%) 
211< 0.1%
 
50.491< 0.1%
 
70.081< 0.1%
 
137.381< 0.1%
 
156.671< 0.1%
 
ValueCountFrequency (%) 
1193.981< 0.1%
 
1185.131< 0.1%
 
1183.741< 0.1%
 
1182.991< 0.1%
 
1177.371< 0.1%
 

sta_press
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct537
Distinct (%)0.5%
Missing104548
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean22.68769666
Minimum0
Maximum25.71
Zeros2
Zeros (%)< 0.1%
Memory size1.6 MiB
2023-01-11T10:47:46.708288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.79
Q122.9
median23.2
Q324.53
95-th percentile24.88
Maximum25.71
Range25.71
Interquartile range (IQR)1.63

Descriptive statistics

Standard deviation2.347370415
Coefficient of variation (CV)0.1034644658
Kurtosis0.4917023691
Mean22.68769666
Median Absolute Deviation (MAD)1.12
Skewness-1.326638013
Sum2255724.24
Variance5.510147864
MonotocityNot monotonic
2023-01-11T10:47:46.843282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23.1717610.9%
 
23.1417250.8%
 
23.114960.7%
 
23.214870.7%
 
23.0714760.7%
 
23.2714100.7%
 
23.2413940.7%
 
23.0413500.7%
 
23.3111860.6%
 
17.8911520.6%
 
Other values (527)8498841.7%
 
(Missing)10454851.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
17.373< 0.1%
 
17.381< 0.1%
 
17.392< 0.1%
 
17.45< 0.1%
 
ValueCountFrequency (%) 
25.711< 0.1%
 
25.72< 0.1%
 
25.672< 0.1%
 
25.661< 0.1%
 
25.571< 0.1%
 

altimeter_setting
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct691
Distinct (%)0.7%
Missing104548
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean28.88638672
Minimum0
Maximum33.35
Zeros2
Zeros (%)< 0.1%
Memory size1.6 MiB
2023-01-11T10:47:46.958521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.07
Q129.66
median29.96
Q330.15
95-th percentile31.47
Maximum33.35
Range33.35
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation2.690465918
Coefficient of variation (CV)0.09313957968
Kurtosis0.974998366
Mean28.88638672
Median Absolute Deviation (MAD)0.22
Skewness-1.557833259
Sum2872029
Variance7.238606856
MonotocityNot monotonic
2023-01-11T10:47:47.069743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30.0215030.7%
 
30.0614760.7%
 
30.0414750.7%
 
30.0314470.7%
 
29.9914210.7%
 
30.0113950.7%
 
30.0513620.7%
 
3013380.7%
 
30.0813270.7%
 
29.9713170.6%
 
Other values (681)8536441.9%
 
(Missing)10454851.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
22.533< 0.1%
 
22.551< 0.1%
 
22.562< 0.1%
 
22.575< 0.1%
 
ValueCountFrequency (%) 
33.351< 0.1%
 
33.332< 0.1%
 
33.32< 0.1%
 
33.291< 0.1%
 
33.171< 0.1%
 

solar_radiation
Real number (ℝ≥0)

MISSING
ZEROS

Distinct990
Distinct (%)1.0%
Missing103039
Missing (%)50.5%
Infinite0
Infinite (%)0.0%
Mean127.3597301
Minimum0
Maximum1067
Zeros51952
Zeros (%)25.5%
Memory size1.6 MiB
2023-01-11T10:47:47.186461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3165
95-th percentile664
Maximum1067
Range1067
Interquartile range (IQR)165

Descriptive statistics

Standard deviation217.6020009
Coefficient of variation (CV)1.708562045
Kurtosis2.623684614
Mean127.3597301
Median Absolute Deviation (MAD)0
Skewness1.882608122
Sum12854927
Variance47350.63079
MonotocityNot monotonic
2023-01-11T10:47:47.296403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05195225.5%
 
24010.2%
 
33750.2%
 
53490.2%
 
43460.2%
 
13440.2%
 
63230.2%
 
113130.2%
 
82910.1%
 
92910.1%
 
Other values (980)4594922.5%
 
(Missing)10303950.5%
 
ValueCountFrequency (%) 
05195225.5%
 
13440.2%
 
24010.2%
 
33750.2%
 
43460.2%
 
ValueCountFrequency (%) 
10671< 0.1%
 
10561< 0.1%
 
10241< 0.1%
 
10202< 0.1%
 
10091< 0.1%
 

pct_possible
Categorical

HIGH CARDINALITY
MISSING

Distinct102
Distinct (%)0.1%
Missing103039
Missing (%)50.5%
Memory size1.6 MiB
--
51952 
100 %
 
1271
8 %
 
976
10 %
 
966
9 %
 
946
Other values (97)
44823 
ValueCountFrequency (%) 
--5195225.5%
 
100 %12710.6%
 
8 %9760.5%
 
10 %9660.5%
 
9 %9460.5%
 
11 %9410.5%
 
5 %9370.5%
 
13 %9240.5%
 
7 %8800.4%
 
17 %8700.4%
 
Other values (92)4027119.7%
 
(Missing)10303950.5%
 
2023-01-11T10:47:47.444798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-11T10:47:47.558891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length2.958469994
Min length2

Interactions

2023-01-11T10:47:22.758722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:22.847555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:22.912038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.084658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.162589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.233806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.298885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.364753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.430554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.500430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.566473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.634707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.700681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.765970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.832122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.895236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:23.961760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.024285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.086773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.150505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.214697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.279130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.343351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.407733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.472402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.538597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.603669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.676956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.747676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.820240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.905387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:24.992369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.065634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.251710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.345101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.418710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.484919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.554053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.638028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.714119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.789020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.871469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:25.953343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.035964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.104659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.170422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.247631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.320401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.394669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.473432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.540345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.606817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.673190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.739624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.805656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.870769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:26.944281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.013657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.084244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.151797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.227175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.302092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.371533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.442016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.520426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.598504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.677078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.749101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.814787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:27.878347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.103465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.190486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.260539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.332116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.397329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.461483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.524506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.587569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.654092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.724246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.788241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.853685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:28.927381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.005742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.081456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.158402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.234199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.307580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.382695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.460014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.537890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.617742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.698236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.772216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.836767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.905274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:29.978087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.048160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.114881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.182177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.257377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.334196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.411630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.493411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.573574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.648060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.725410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.797707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.868434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:30.938327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.012392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.104554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.174636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.244515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.317477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.391985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.638023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.728957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.808563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.889409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:31.967738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.050159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.138189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.245051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.344539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.432610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.509313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.578105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.647876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.716290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.795331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.897864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:32.965778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.039094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.228859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.361873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.460842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.554600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.644888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.734046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.831751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.907289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:33.983233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.081679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.187018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.270304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.353037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.437399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.519457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.597389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.666628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.736668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.804078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.882341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:34.963403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.043373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.128874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.216401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.305159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.396089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.549844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.681502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.757086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.835755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:35.960473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.125802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.268775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.376401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.490068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.576022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.655965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:36.725855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.011394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.094006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.167164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.245162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.325274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.405550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.502426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.583705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.662463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.738458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.815046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.883263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:37.958494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.032675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.102396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.177931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.271639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.380020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.484323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.584268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.667432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.751749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.830680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:38.908446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-11T10:47:47.665278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-11T10:47:47.894965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-11T10:47:48.106870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-11T10:47:48.291000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-01-11T10:47:48.493437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-01-11T10:47:39.388147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:40.220478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:41.575412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T10:47:42.127244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

dttempdew_ptrHheat_idxwind_chillwind_dirwind_spdhr_precipsnow_depthsnowfall_3hrsnowfall_6hrsnowfall_24hrsitefile_nameyearsea_lvl_presssta_pressaltimeter_settingsolar_radiationpct_possible
02022-12-23 04:00:00-25.0-31.072.0NaNNaNESE1G40.012.60.10.10.3JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
12022-12-23 03:00:00-26.0-32.072.0NaNNaNESE1G30.012.60.10.10.4JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
22022-12-23 02:00:00-27.0-33.071.0NaNNaNESE1G40.012.50.10.00.3JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
32022-12-23 01:00:00-28.0-34.072.0NaNNaNESE2G50.012.50.00.00.2JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
42022-12-23 00:00:00-29.0-35.071.0NaNNaNESE2G40.012.50.00.00.2JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
52022-12-22 23:00:00-28.0-34.071.0NaNNaNE2G50.012.40.00.00.0JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
62022-12-22 22:00:00-28.0-34.071.0NaNNaNESE2G50.012.50.00.00.2JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
72022-12-22 21:00:00-28.0-34.072.0NaNNaNESE2G40.012.50.00.10.0JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
82022-12-22 20:00:00-27.0-33.073.0NaNNaNESE2G40.012.60.10.50.2JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN
92022-12-22 19:00:00-26.0-32.073.0NaNNaNSE1G50.012.50.00.70.1JVEMTJVEMT-2022.html2022NaNNaNNaNNaNNaN

Last rows

dttempdew_ptrHheat_idxwind_chillwind_dirwind_spdhr_precipsnow_depthsnowfall_3hrsnowfall_6hrsnowfall_24hrsitefile_nameyearsea_lvl_presssta_pressaltimeter_settingsolar_radiationpct_possible
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2039642022-01-01 08:00:00-1.0NaNNaNNaN-24.0S21G37NaN38.30.00.0NaNMRPMTMRPMT-2022.html2022NaN24.6029.83NaNNaN
2039652022-01-01 07:00:00-1.0NaNNaNNaN-24.0S21G34NaN39.20.00.0NaNMRPMTMRPMT-2022.html2022NaN24.6029.83NaNNaN
2039662022-01-01 06:00:00-1.0NaNNaNNaN-24.0S22G33NaN38.80.00.0NaNMRPMTMRPMT-2022.html2022NaN24.6029.82NaNNaN
2039672022-01-01 05:00:00-1.0NaNNaNNaN-25.0SSE24G35NaN39.70.00.0NaNMRPMTMRPMT-2022.html2022NaN24.5929.81NaNNaN
2039682022-01-01 04:00:00-4.0NaNNaNNaN-26.0S18G31NaN40.80.10.0NaNMRPMTMRPMT-2022.html2022NaN24.6029.82NaNNaN
2039692022-01-01 03:00:00-5.0NaNNaNNaN-26.0S15G21NaN41.50.50.3NaNMRPMTMRPMT-2022.html2022NaN24.5929.81NaNNaN
2039702022-01-01 02:00:00-3.0NaNNaNNaN-23.0SSW15G25NaN40.90.00.4NaNMRPMTMRPMT-2022.html2022NaN24.5929.81NaNNaN
2039712022-01-01 01:00:00-4.0NaNNaNNaN-22.0SW12G19NaN40.70.01.0NaNMRPMTMRPMT-2022.html2022NaN24.5729.79NaNNaN
2039722022-01-01 00:00:00-7.0NaNNaNNaN-26.0SSW12G20NaN41.00.02.0NaNMRPMTMRPMT-2022.html2022NaN24.5729.79NaNNaN